Trajectory Tracking Of Robot Manipulators Research Articles

Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems

Computed torque control is an effective control scheme for trajectory tracking of robotic manipulators. However, computed torque control requires precise dynamic models of robotic manipulators and is severely affected by uncertain dynamics. Thus, a new scheme that combines a computed torque control and a novel model-assisted extended state observer is developed for the robust tracking control of robotic manipulators subject to structured and unstructured uncertainties to overcome the disadvantages of computed torque control and exploit its merits. The model-assisted extended state observer is designed to estimate and compensate these uncertain dynamics as a lumped disturbance online, which further improves the disturbance rejection property of a robotic system. Global uniform ultimate boundedness stability with an exponential convergence of a closed-loop system is verified through Lyapunov method. Simulations are performed on a two degree-of-freedom manipulator to verify the effectiveness and superiority of the proposed controller.

Practical Time-Delay Control With Adaptive Gains for Trajectory Tracking of Robot Manipulators

This paper presents a practical adaptive time-delay control scheme (ATDC) and then applies it to robot manipulators. The proposed ATDC uses a time-delayed signal to cancel complex nonlinear dynamics and disturbances, and hence to provide a simple structure. The adaptive law of the proposed ATDC is developed to adjust the control gains appropriately while in operation and then to achieve good tracking performance with tolerant fluctuation and fast convergence speed. Such adaptive control gains are shown to have an effect of adding an adaptive auxiliary control to a conventional time-delay control (TDC). Moreover, the proposed ATDC includes a typical sliding-mode control as a second auxiliary control to suppress the time-delay estimation (TDE) errors caused by the time-delayed signal so that robust performance can be achieved. It is shown by the bounded input bounded output stability theory and a Lyapunov approach that the TDE errors are bounded and the tracking errors are uniformly ultimately bounded, respectively. The practical effectiveness of the proposed ATDC, or the synergistic effect of two auxiliary controls, is illustrated in simulations and experiments with robot manipulators.

Fuzzy PID based adaptive control on industrial robot system

An importance of Industrial robots have been increasing in many manufacturing firms to maintain quality and productivity. In this paper a fuzzy pid based trajectory tracking of robot manipulator for different structures is proposed which make use for different process with minimum error and these scenario are analyzed using a dynamic robot model. The simulation of fuzzy-pid based control of 6DOF industrial robot shows the better performance of the orientation of the end effectors and joint angular parameters for different structure when compared with the pid controller.

Iterative Learning Control for Trajectory Tracking of Robot Manipulators

Iterative learning control (ILC) has been shown to be effective in improving tracking performance of repetitive tasks, and is widely used in the motion control systems of CNC machines, semiconductor manufacturing equipment, hard disk drives, etc. However, applying ILC to robot manipulators requires careful consideration of nonlinear dynamics. We propose using a computed torque controller and the disturbance observer (DOB) to robustly linearize the dynamics of robot manipulators. The PD feedback controller is then applied for each joint to achieve the desired bandwidth and damping ratio. Both control-based ILC and command-based ILC are implemented separately in the linearized system as a feedforward compensator to enhance trajectory tracking accuracy. The proposed control system is realized in a six-axis industrial robot. Experimental results show that DOB is indispensable for robust feedback linearization so that ILC can work on the linearized system to improve the tracking performance for repetitive motion. Satisfactory and similar performance is accomplished by both control-based ILC and command-based ILC.

Iterative learning control for non-repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults

In this work, we present a novel iterative learning control ILC scheme for a class of joint position constrained robot manipulator systems with both multiplicative and additive actuator faults. Unl...

Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three Links Cylindrical Robot

A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.

Uncertain polytopic LPV modelling of robot manipulators and trajectory tracking

This research work proposes a full state systematic feedback control design method for some classes of non-linear systems which are forced to follow a specific desired trajectory, such as robotic systems, using uncertain polytopic linear parameter-varying (LPV) modelling approach. An LPV representation of the system is generated from linearization of its usual Lagrangian equation about a desired state trajectory and is reduced to an uncertain polytopic one. A vector of scheduling signals from the desired trajectory information is produced to construct the LPV model. The control gain matrix is derived by solving a set of linear matrix inequalities (LMIs) that returns the sufficiently small value of the time derivative of the Lyapunov function. A sufficient condition is proposed to guarantee the asymptotic stability of the closed-loop LPV systems against the uncertainties on the vertices. The proposed scheme is applied to controller synthesis of a two-degree-of-freedom robotic manipulator trajectory tracking problem.

Iterative learning control for non‐repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults

SummaryIn this work, we present a novel iterative learning control (ILC) scheme for a class of joint position constrained robot manipulator systems with both multiplicative and additive actuator faults. Unlike most ILC literature that requires identical reference trajectory from trail to trail, in this work the reference trajectory can be non‐repetitive over the iteration domain without assuming the identical initial condition. A tan‐type Barrier Lyapunov Function is proposed to deal with the constraint requirements which can be both time and iteration varying, with ILC update laws adopted to learn the iteration‐invariant system uncertainties, and robust methods used to compensate the iteration and time varying actuator faults and disturbances. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraint requirements on the joint position vector will not be violated during operation. An illustrative example on a two degree‐of‐freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.

A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks

This paper presents a novel adaptive terminal sliding mode controller for the trajectory tracking of robotic manipulators using radial basis function neural networks (RBFNNs). First, a modified terminal sliding mode (TSM) surface is approached to avoid the singularity problem of conventional TSM. Then, a nonsingular TSM control is designed for joint position tracking of a robotic manipulator. In the control scheme, fully tuned RBFNNs are adopted to approximate the nonlinear unknown dynamics of the robotic manipulator. Adaptive learning algorithms are derived to allow online adjustment of the output weights, the centers and the variances in the RBFNNs. Meanwhile, a continuous robust control term is added to eliminate chattering efforts in the sliding mode control (SMC) system. The stability and finite-time convergence of the closed-loop system are established by using Lyapunov theory. Finally, the simulation results of a two-link robotic manipulator are presented to demonstrate the effectiveness of the proposed control method.

Discrete-time sliding mode neuro-adaptive controller for SCARA robot arm

This work presents a discrete-time sliding mode neuro-adaptive control (DTSMNAC) method for robot manipulators. Due to the dynamics variations and uncertainties in the robot model, the trajectory tracking of robot manipulators has been one of the research areas for the last years. The proposed control structure is a practical design that combines a discrete-time neuro-adaptation technique with sliding mode control to compensate the dynamics variations in the robot. Using an online adaptation technique, a DTSMNAC controller is used to approximate the equivalent control in the neighborhood of the sliding surface. A sliding control is included to guarantee that the discrete-time neural sliding mode control can improve a stable closed-loop system for the trajectory tracking control of the robot with dynamics variations. The proposed technique simultaneously ensures the stability of the adaptation of the neural networks and can be obtained a suitable equivalent control when the parameters of the robot dynamics are unknown in advance. This neural adaptive system is applied to a SCARA robot manipulator and shows to be able to ensure that the output tracking error will converge to zero. Finally, experiments on a SCARA robot have been developed to show the performance of the proposed technique, including the comparison with a PID controller.

Study on Predictive Control for Trajectory Tracking of Robotic Manipulator

Study on Predictive Control for Trajectory Tracking of Robotic Manipulator

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

Iterative Feedback Tuning for Robotic Manipulator Trajectory Tracking

Iterative Feedback Tuning for Robotic Manipulator Trajectory Tracking

Trajectory Tracking Control of Robot Manipulators

In this paper a simulink based approach is developed for trajectory tracking of robot manipulator. A robot manipulator is widely used in many industrial application.A robot manipulator moves the end effector to the configuration instructed by the user. The input from the main unit is transformed in to the desired configuration through forward kinematics. This configuration is sent to the robot controller to transform the configuration into joint angles. The simulink model is developed to provide basic block to model kinematics and trajectory tracking of robot manipulator. Availability of such library model of robot manipulator software, where the manipulator controller can be modelled using model library blocks and production can be automatically generated using existing code generators for simulink. In this the desired and the actual trajectory of the end effector under different conditions is shown with the help of MATLAB simulation.

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.

Decentralized Adaptive Robust Control of Robot Manipulators Using Disturbance Observers

In this paper, we propose a decentralized adaptive robust controller for trajectory tracking of robot manipulators. In each local controller, a disturbance observer (DOB) is introduced to compensate for the low-passed coupled uncertainties, and an adaptive sliding mode control term is employed to handle the fast-changing components of the uncertainties beyond the pass-band of the DOB. In contrast to most of the local controllers using DOB for robot manipulators that are based on linear control theory, in this study, by some special nonlinear damping terms, the boundedness of the signals of the overall nonlinear system is first ensured. This paves the way to analyze how the DOB and adaptive sliding mode control play in a cooperative way in each local subsystem to achieve an excellent control performance. Simulation results are provided to support the theoretical results.

Adaptive backstepping trajectory tracking control of robot manipulator

An adaptive backstepping control scheme is proposed for task-space trajectory tracking of robot manipulators in the presence of uncertain parameters and external disturbances. In the case of external disturbance-free, the developed controller guarantees that the desired trajectory is globally asymptotically followed. Moreover, taking disturbances into consideration, the controller is synthesized by using adaptive technique to estimate the system uncertainties. It is shown that L2 gain of the closed-loop system is allowed to be chosen arbitrarily small so as to achieve any level of L2 disturbance attenuation. The associated stability proof is constructive and accomplished by the development of a Lyapunov function candidate. Numerical simulation results are included to verify the control performance of the control approach derived.

An Adaptive Controller Dominant-Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

This paper proposes a new hybrid adaptive and learning control method based on combining model-based adaptive control, repetitive learning control (RLC) and proportional–derivative control to consider the periodic trajectory tracking problem of robot manipulators. The aim of this study is to obtain a high-accuracy trajectory tracking controller by developing a simpler adaptive dominant-type hybrid controller by using only one vector for estimation of the unknown dynamical parameters in the control law. The RLC input is adopted using the original learning control law, adding a forgetting factor to achieve the convergence of the learning control input to zero. We will improve and prove that the adaptive dominant-type controller could be applied for tracking a periodic desired trajectory in which adaptive control input increases and becomes dominant of the control input, whereas the other control inputs decrease close to zero. The domination of the adaptive control input gives the advantage that the proposed controller could adjust the feed-forward control input immediately and it does not spend much time relearning the learning control input when the periodic desired trajectory is switched over from the first trajectory to another trajectory. We utilize the Lyapunovlike method to prove the stability of the proposed controller and computer simulation results to validate the effectiveness of the proposed controller in achieving the accurate tracking to the periodic desired trajectory.

슬라이딩 표면을 이용한 로봇 매니퓰레이터의 강건한 적응 마찰 제어

In this paper, a robust adaptive controller is proposed for trajectory tracking of robot manipulators with the unknown friction coefficient and bounded disturbance. A new adaptive control law is developed based on sliding mode and derived from the Lyapunov stability analysis. The introduction of a boundary layer solves the problem of chattering. The proposed adaptive controller is globally asymptotically stable and guarantees zero steady state error for joint positions. The estimated friction coefficients can also approach the actual coefficients asymptotically. A simulation example is provided to demonstrate the performance of the proposed algorithm.

A Research of Trajectory Tracking of Robot Manipulator Using the Compensator of Neural Network Based on Dynamics

In this paper, a new trajectory control approach of robot manipulator based on dynamics model was proposed. The method was a combination of the torque control and a compensator of neural network. The torque profile was updated by the previous torque profile. Errors were inevitable while the modeling for dynamics of robot manipulator, but they could be compensated by the compensation structure of ANN. As such, the trajectory tracking converged very fast and the new algorithm achieved the asymptotical convergence based on the Lyapunov’s method. Furthermore, it could enhance the performance of the adaptability. The result of simulations and experiments show that the controller can get the performance of trajectory well.